Container Migration Performance Evaluation: An Approach Based on Stochastic Petri Nets - Feitosa, Leonel; Rego, Paulo; Silva, Francisco Airton (Anais Estendidos do Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos (SBRC), 2025)
Containerization has emerged as a transformative technology in modern data centers, enabling efficient resource management and improving operational flexibility across various applications. While often associated with platforms like Docker and Kubernetes, container-based solutions are widely integrated into cloud environments such as AWS, Microsoft Azure, and Google Cloud. In large-scale distributed systems, efficient container migration is crucial to manage server downtime, consolidate resources, and ensure reliability in mobile edge computing scenarios. The problem with evaluating container migration performance lies in the high cost and computational demand of real-world experiments. Assessing different migration strategies efficiently remains a challenge, particularly for stateful containers, which require structured modeling approaches to quantify their impact on system performance. The proposal of this study is to develop Stochastic Petri Net (SPN) models to assess container migration strategies. The approach includes two models—one incorporating an absorbing state and another without—analyzing key migration techniques: Cold, PreCopy, PostCopy, and Hybrid. These models evaluate critical performance metrics, including Migration Total Time (MTT), Mean Migration Time (MMT), utilization, discard probability, and migration rate. Furthermore, a sensitivity analysis based on the Design of Experiments (DoE) was conducted for the Hybrid migration strategy to identify key performance factors. The conclusion of this research is that by providing an analytical framework for container migration evaluation, it enhances the understanding of migration performance dynamics and supports decision-making in cloud and edge computing infrastructures.